|Model||Max Tokens||Training Data|
|GPT-4 Turbo||128,000 tokens||Up to Apr 2023|
|GPT-4 Turbo with vision||128,000 tokens||Up to Apr 2023|
|gpt-4||8,192 tokens||Up to Sep 2021|
|gpt-4-0613||8,192 tokens||Up to Sep 2021|
|gpt-4-32k||32,768 tokens||Up to Sep 2021|
|gpt-4-32k-0613||32,768 tokens||Up to Sep 2021|
|gpt-3.5-turbo-1106||16,385 tokens||Up to Sep 2021|
|gpt-3.5-turbo||4,096 tokens||Up to Sep 2021|
|gpt-3.5-turbo-16k||16,384 tokens||Up to Sep 2021|
|gpt-3.5-turbo-instruct||4,097 tokens||Up to Sep 2021|
|gpt-3.5-turbo-0613||4,096 tokens||Up to Sep 2021|
|gpt-3.5-turbo-16k-0613||16,384 tokens||Up to Sep 2021|
|text-davinci-003||4,097 tokens||Up to Jun 2021|
|text-davinci-002||4,097 tokens||Up to Jun 2021|
|code-davinci-002||8,001 tokens||Up to Jun 2021|
This article aims to illuminate an integral yet less-explored facet of AI language models, the token limit. The goal is to equip readers with a thorough understanding of token limits in the context of AI language models and their significance in the continued development and utilization of these advanced tools.
Table Of Contents
What Is Token
In the context of natural language processing (NLP) and language models, a “token” represents the most fundamental unit of data that the model is designed to handle. A token can be as small as a single character or as large as a word, depending on the specifics of the language and the model’s design.
In AI language models like GPT-4, a token often corresponds to a single word, but it can also represent a part of a word, a whole phrase, or even punctuation or whitespace. For example, in the sentence “ChatGPT is an AI model,” there would be five tokens: “ChatGPT,” “is,” “an,” “AI,” “model,” and the punctuation “.”.
In essence, tokens are the “building blocks” that language models use to understand and generate text. They form the basis of the input and output data, and the quantity, variety, and quality of tokens directly influence the effectiveness of the model’s performance.
Why There is a Token Limit in GPT Models
OpenAI’s GPT Models operate on a token limit due to several reasons that revolve around efficiency, computational feasibility, and model performance:
- Computational Efficiency: Handling vast amounts of tokens concurrently demands significant computing resources, including memory and processing power. Setting a token limit helps manage the computational cost and ensures the language model operates within reasonable timeframes, providing timely responses.
- Model Performance: A token limit helps maintain the quality of output. As language models like GPT-3 or GPT-4 generate responses based on the context of previous tokens, the more tokens it processes, the higher the chance for the model to lose track of the initial context, potentially affecting the coherence of the generated text.
- Memory Limitations: The token limit is inherently tied to the architecture of the neural networks used in language models. For instance, transformer-based models like GPT-3 and GPT-4 have a fixed-size attention window due to their architecture. This determines how many tokens the model can ‘remember’ or pay attention to at once.
- Resource Allocation: Setting a token limit helps balance resource usage among multiple simultaneous users, ensuring fair access to computational resources in a multi-user environment.
Token Limit in GPT-4
|Latest model||Description||Max tokens||Training data|
|GPT-4 Turbo||The latest GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens.||128,000 tokens||Up to Apr 2023|
|GPT-4 Turbo with vision||Ability to understand images, in addition to all other GPT-4 Turbo capabilties. Returns a maximum of 4,096 output tokens.||128,000 tokens||Up to Apr 2023|
|gpt-4||More capable than any GPT-3.5 model, able to do more complex tasks, and optimized for chat.||8,192 tokens||Up to Sep 2021|
|gpt-4-0613||Snapshot of ||8,192 tokens||Up to Sep 2021|
|gpt-4-32k||Same capabilities as the base ||32,768 tokens||Up to Sep 2021|
|gpt-4-32k-0613||Snapshot of ||32,768 tokens||Up to Sep 2021|
Token Limit in GPT-3.5
|Latest model||Description||Max tokens||Training data|
|gpt-3.5-turbo-1106||The latest GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens.||16,385 tokens||Up to Sep 2021|
|gpt-3.5-turbo||Most capable GPT-3.5 model and optimized for chat at 1/10th the cost of ||4,096 tokens||Up to Sep 2021|
|gpt-3.5-turbo-16k||Same capabilities as the standard ||16,384 tokens||Up to Sep 2021|
|gpt-3.5-turbo-instruct||Similar capabilities as ||4,097 tokens||Up to Sep 2021|
|gpt-3.5-turbo-0613||Snapshot of ||4,096 tokens||Up to Sep 2021|
|gpt-3.5-turbo-16k-0613||Snapshot of ||16,384 tokens||Up to Sep 2021|
|text-davinci-003||Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models. Also supports some additional features such as inserting text.||4,097 tokens||Up to Jun 2021|
|text-davinci-002||Similar capabilities to ||4,097 tokens||Up to Jun 2021|
|code-davinci-002||Optimized for code-completion tasks||8,001 tokens||Up to Jun 2021|
How Token Limit Can Affect the Utility of ChatGPT
While the token limit is necessary for practical and computational reasons, it does pose constraints on the utility of ChatGPT, affecting its applicability in various scenarios. Understanding these constraints can help in designing applications and interfaces that effectively work within these limits while still delivering valuable results.
- Conversation Length: The most direct impact of the token limit is on the length of the conversations that can be handled. Both the input and the output count towards the token limit, meaning longer conversations may not fit within the limit, necessitating the trimming or omission of some parts.
- Contextual Understanding: Since the token limit also defines the ‘memory’ of a language model, it can influence the model’s contextual understanding. If a conversation exceeds the token limit, the model may lose the earlier parts of the context, which could lead to less relevant or coherent responses.
- Comprehensive Responses: The token limit can also constrain the model’s ability to provide more comprehensive, elaborate responses. For example, if the token limit is close to being reached, the model will have to generate shorter responses, potentially reducing the depth or detail of the information provided.
- Multi-Turn Conversations: For dialogues involving many back-and-forths or multiple participants, the token limit can become a critical factor. The conversation needs to fit within the model’s token limit, which might be challenging with many conversational turns.
- Real-Time Interactions: In real-time applications where rapid responses are needed, the time taken to process a large number of tokens can become significant, affecting the user experience.
What The Differences Between Rate Limits And Token Limits
Rate limits restrict the number of API requests. Token limits restrict the number of tokens (usually words) sent to a model per request. For example, gpt-4-32k-0613 has a max of 32,768 tokens per request. You can’t increase the token limit, only reduce the number of tokens per request.
See Also: What Are The Rate Limits For OpenAI API?
How to Get Around OpenAI Token Limits
Despite the necessity of token limits in GPT models, developers have identified several strategies to manage and overcome these constraints, ensuring efficient and effective use of ChatGPT:
- Condensing Input: Developers can pre-process and condense inputs to the model, summarizing information or removing unnecessary details. This helps in preserving the most important context within the token limit.
- Truncation: In cases where the input exceeds the token limit, developers can truncate the text to fit within the limit. While this might result in the loss of some information, careful truncation can help maintain the essential context.
- Continuation: If a conversation or text analysis task exceeds the token limit, it can be broken down into smaller parts, each processed separately and in sequence. This can be helpful for tasks like document analysis.
- Optimized Model Design: Researchers are constantly working on designing more optimized models and techniques to handle token limits better, such as Sparse Transformers that can handle more tokens within the same computational constraints.
- Prompt Engineering: Careful crafting of the model’s prompt can help elicit more concise and to-the-point responses, conserving tokens for further conversation.
- Model Customization: When possible, advanced users can potentially adjust the model’s parameters to better manage the token limit, such as tweaking the ‘temperature’ parameter to control the randomness of output and the length of the responses.
The Future of Token Limits in GPT Models
The future of token limits in GPT models is a dynamic field, intertwined with the broader trajectory of AI research and technological advancements. Here are a few possibilities and directions the future might hold:
- Higher Token Limits: As computational power increases and model architectures become more efficient, future GPT models might feature higher token limits, allowing for longer conversations and more complex tasks to be handled.
- Improved Handling of Tokens: Innovations in AI could lead to more effective token management, such as improved contextual understanding over long token sequences or more efficient handling of tokens within existing computational constraints.
- Beyond the Token Concept: Future research might evolve beyond the concept of tokens altogether. New paradigms, such as models based on byte or character-level processing or even entirely novel concepts, could revolutionize how language models process text.
- Personalized Token Limits: We might see the development of personalized token limits, dynamically adapting based on the task’s nature or the available computational resources.
- Balancing Act: The future of token limits will likely continue to be a balancing act between computational feasibility, model performance, and practical utility. How this balance is struck might vary based on the use case, user requirements, and technological advancements.
Please note that as AI research advances, the community’s understanding of token limits and their implications will continue to evolve, shaping the future development and use of GPT models. As of now, we can only speculate on these possibilities, with the actual future likely to bring surprises and innovations beyond our current anticipation.